Hardware is a crucial part of a computer system that defines its physical capabilities, but it is software (and the creativity behind it) that exploits those capabilities and makes great and useful applications. There currently exists a vast array of programming languages; some popular ones include C, C++, Python, and Java, and many more continue to be developed. Different languages are often used for different applications and environments, but at a fundamental level, software can be characterized as a set of instructions that manipulate data (i.e., ones and zeros) in memory. Nevertheless, computer programming can be complicated, especially for large projects that require teams of coders each working on a different aspect of an overall application.
In some environments, such as virtual machines, challenges abound in the management of software and hardware interactions. One such challenge is the difficulty associated with accessing, analyzing, and/or modifying data located in memory that is remote to the memory space of a task requesting access. The underlying problem is that most programming languages assume and require direct operations upon memory (e.g. to dereference pointers, access relative-offset memory addresses, etc.), and thus interaction with data is limited to the memory space that is directly addressable by the executing task. Compiled software also conforms to this basic assumption because the syntax of most programming languages assumes only localized memory space access, and so the syntax cannot be used to interact with remote memory.
One conventional technique to address this issue involves using a hypervisor to facilitate inter-virtual-machine access. In this case, an application in a first virtual machine may request a hypervisor to read memory allocated for an application within a second virtual machine (or, in other words, remote memory), and the hypervisor may confirm that the first application has privileges to access that memory. In a simpler non-virtualized scenario, a first application may request from its OS kernel access to the memory of a second application; if allowed, the OS will facilitate access to the requested data. Once access has been granted and facilitated by a hypervisor or OS to remote memory, data in remote memory must be accurately interpreted. This technique, however, requires detailed knowledge of data structures used in remote memory, memory layout, application/OS versions, and virtual machine configurations and is difficult to implement. Particularly, explicit and complex remote memory addressing/interpreting instructions must be included within the program source code of the first application. These instructions retrieve data from a source system and translate that data by mimicking operations otherwise implicitly performed by a computer program compiler or interpreter. The translation step is achieved via memory addressing calculations and facilitated by metadata (i.e., information that describes the boundaries of a remote memory space and the data structures and types residing within that space, and that permits correct interpretation of memory layout). All of this adds a high degree of complexity to the source code, makes the development process inefficient and exhausting, and yields code that is challenging to read and debug. For example, what might normally be accomplished in local memory by a simple line of code in native language syntax can often require an additional ten to twenty lines or more of code just to handle the necessary remote memory addressing calculations.
Accordingly, it would be advantageous to provide systems and methods that address the aforementioned problems.